A Survey of Graph Meets Large Language Model: Progress and Future Directions
It provides a comprehensive overview for researchers working on graph-related applications, but it is incremental as it synthesizes existing studies rather than introducing new methods.
This survey reviews and analyzes methods that integrate Large Language Models (LLMs) with graphs for tasks like citation and social networks, proposing a new taxonomy to categorize these approaches and highlighting state-of-the-art performance improvements over traditional Graph Neural Networks (GNNs).
Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods and yield state-of-the-art performance. In this survey, we first present a comprehensive review and analysis of existing methods that integrate LLMs with graphs. First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. Then we systematically survey the representative methods along the three categories of the taxonomy. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. The relevant papers are summarized and will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.